Statistics collection for network traffic

ABSTRACT

A system architecture for thwarting denial of service attacks on a victim data center is described. The system includes a first plurality of monitors that monitor network traffic flow through the network. The first plurality of monitors is disposed at a second plurality of points in the network. The system includes a central controller that receives data from the plurality of monitors, over a hardened, redundant network. The central controller analyzes network traffic statistics to identify malicious network traffic. In some embodiments of the system, a gateway device is disposed to pass network packets between the network and the victim site. The gateway is disposed to protect the victim site, and is coupled to the control center by the redundant hardened network.

This application claims the benefit of U.S. Provisional Patent application Ser. No. 60/230,759, filed Sep. 7, 2000, entitled “THWARTING DENIAL OF SERVICE ATTACKS”

BACKGROUND

This invention relates to techniques to thwart network-related denial of service attacks.

In denial of service attacks, an attacker sends a large volume of malicious traffic to a victim. In one approach an attacker, via a computer system connected to the Internet infiltrates one or a plurality of computers at various data centers. Often the attacker will access the Internet through an Internet Service Provider (ISP). The attacker by use of a malicious software program places the plurality of computers at the data centers under its control. When the attacker issues a command to the computers at the data centers, the machines send data out of the data centers at arbitrary times. These computers can simultaneously send large volumes of data over various times to the victim preventing the victim from responding to legitimate traffic.

SUMMARY

According to an aspect of the present invention, a method of monitoring traffic flow in a monitor device disposed to receive network traffic packets includes producing statistics corresponding to a parameter of traffic flow to trace the source of an attack. The method also includes mapping the traffic flow into a plurality of buckets by applying a hash function “f(h)” to the parameter of the traffic flow to output an integer corresponding to one of the buckets and accumulating statistics from the packets. The method includes comparing the number of buckets to a threshold and determining whether the number of buckets should be divided into more buckets or combined into fewer buckets based on comparing the number of buckets to the threshold.

According to an additional aspect of the present invention, a computer program product residing on a computer readable for monitoring network traffic flow in a network includes instructions to cause a computer to map traffic flow into a plurality of buckets by applying a hash function “f(h)” to a parameter of the traffic flow to output an integer corresponding to one of the buckets. The program accumulates statistics from the packets and compares the accumulated statistic values from the buckets to configured threshold values corresponding to the number of buckets to determine that an event is of significance. The program adjusts the number of buckets as the number of buckets approaches a second threshold.

According to an additional aspect of the present invention, a data collector to collect statistical information about network flows includes a computer readable medium and a computing device that executes a computer program product stored on the computer readable medium. The computer program product includes instructions to cause the computing device to map traffic flow into a plurality of buckets by applying a hash function “f(h)” to the parameter of the traffic flow to output an integer corresponding to one of the buckets and accumulate statistics from the packets. The program compares the accumulated statistic values from the buckets to configured threshold values corresponding to the number of buckets to determine that an event is of significance and adjusts the number of buckets as the number of buckets approaches a second threshold. The data collector also includes a port to link the data collector to a central control center.

One or more aspects of the invention may provide some or all of the following advantages.

An attack may be designed to explode the number of buckets. Since each bucket consumes memory space, the attack can be designed to consume all available memory and crash the data collection processes and devices.

The invention prevents that type of attack setting thresholds on the number of buckets. As buckets approach a bucket threshold “Th”, the process can take several buckets and divide them in more buckets or combine them into fewer buckets. The variable number of buckets dynamically adjusts the monitoring process to the amount of traffic and number of flows, so that monitoring is not vulnerable to a DoS attacks against its own resources.

The variable number of buckets also helps to efficiently identify the source(s) of attack by breaking down traffic into different categories (buckets) and looking at the appropriate parameters and thresholds in each bucket. With the invention, traffic is monitored at multiple levels of granularity, from aggregate to individual flows. The invention can be applied to various types of monitoring, e.g., TCP packet ratios, repressor traffic, and so forth.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of networked computers showing an architecture to thwart denial of service attacks.

FIG. 2 is a block diagram depicting details of placement of a gateway.

FIG. 3 is a block diagram depicting details of placement of data collectors.

FIG. 4 is flow chart depicting a data collection process.

FIG. 5 is a flow chart depicting details of a control center.

FIG. 6 is a diagram depicting functional layers of a monitoring process.

FIG. 7 is a diagram depicting one technique to gather statistics for use in algorithms that determine sources of an attack.

FIG. 8 is a diagram depicting an alternative technique to gather statistics for use in algorithms that determine sources of an attack.

FIG. 9 is flow chart depicting a process to determine receipt of bad TCP traffic.

FIG. 10 is flow chart depicting a process to defend against setup time connection attacks.

DETAILED DESCRIPTION

Referring to FIG. 1, an arrangement 10 to thwart denial of service attacks (DoS attacks) is shown. The arrangement 10 is used to thwart an attack on a victim data center 12, e.g., a web site or other network site under attack. The victim 12 is coupled to the Internet 14 or other network. For example, the victim 12 has a web server located at a data center (not shown).

An attacker via a computer system (not shown) that is connected to the Internet e.g., via an Internet Service Provider (ISP). (not shown) or other approach, infiltrates one or a plurality of computers at various other sites or data centers 20 a-20 c. The attacker by use of a malicious software program 21 that is generally surreptitiously loaded on the computers of the data centers 20 a-20 c, places the plurality of computers in the data centers 20 a-20 c under its control. When the attacker issues a command to the data centers 20 a-20 c, the data centers 20 a-20 c send data out at arbitrary times. These data centers 20 a-20 c can simultaneously send large volumes of data at various times to the victim 12 to prevent the victim 12 from responding to legitimate traffic.

The arrangement 10 to protect the victim includes a control center 24 that communicates with and controls gateways 26 and data collectors 28 disposed in the network 14. The arrangement protects against DoS attacks via intelligent traffic analysis and filtering that is distributed throughout the network. The control center 24 is coupled to the gateways 26 and data collectors 28 by a hardened, redundant network 30. Gateways 26 and data collectors 28 are types of monitors that monitor and collect statistics on network traffic. In preferred embodiments, the network is inaccessible to the attacker. The gateway 26 devices are located at the edges of the Internet 14, for instance, at the entry points of data centers. The gateway devices constantly analyze traffic, looking for congestion or traffic levels that indicate the onset of a DoS attack. The data collectors 28 are located inter alia at major peering points and network points of presence (PoPs). The data collectors 28 sample packet traffic, accumulate, and collect statistical information about network flows.

All deployed devices e.g., gateways 26 and data collectors 28 are linked to the central control center. The control center aggregates traffic information and coordinates measures to track down and block the sources of an attack. The arrangement uses a distributed analysis emphasizing the underlying characteristics of a DoS attack, i.e., congestion and slow server response, to produce a robust and comprehensive DoS solution. Thus, this architecture 10 can stop new attacks rather than some solutions that can only stop previously seen attacks. Furthermore, the distributed architecture 10 will frequently stop an attack near its source, before it uses bandwidth on the wider Internet 14 or congests access links to the targeted victim 12.

A virus is one way to get attacks started. When surfing the web page a user may download something, which contains a virus that puts the user's computer under the control of some hacker. In the future, that machine can be one of the machines that launches the attack. The attacker only needs a sufficient amount of bandwidth to get a sufficient number of requests out to the victim 12 to be malicious.

Referring to FIG. 2, details of an exemplary deployment of a gateway is shown. Other deployments are possible and the details of such deployments would depend on characteristics of the site, network, cost and other considerations. The gateway 26 is a program executing on a device, e.g., a computer 27 that is disposed at the edge of the data center 20 behind an edge router at the edge of the Internet 14. Additional details on the gateway 26 are discussed below and in the APPENDIX A. In a preferred embodiment, a plurality of gateway devices are deployed at a corresponding plurality of locations, e.g., data centers or sites over the network, e.g., the Internet 14. There can be one gateway or a plurality of gateways at each data center, but that is not necessarily required.

The gateway 26 includes a monitoring process 32 (FIG. 6B) that monitors traffic that passes through the gateway as well as a communication process 33 that can communicate statistics collected in the gateway 26 with the data center 24. The gateway uses a separate interface over a private, redundant network, such as a modem 39 to communicate with the control center 24 over the hardened network 30. Other interface types besides a modem are possible. In addition, the gateway 26 can include processes 35 to allow an administrator to insert filters to filter out, i.e., discard packets that the device deems to be part of an attack, as determined by heuristics described below.

An attack can be designed to either overload the servers or overload some part of the network infrastructure inside the victim site 12. Thus, the victim site 12 can include routers, switches, load balancers and other devices inside the data center that can be targeted by the attack. A particularly troublesome attack causes overload of upstream bandwidth. Upstream bandwidth is the capacity between the victim 12 data center 12 a and one or a plurality of routers or switches belonging to the victim 12 data center's network service provider, which provides connectivity to the rest of the network, e.g., the Internet.

For an exemplary configuration, the victim site 12 can include a plurality of high bandwidth lines feeding a GSR (Gigabit Switch Router). At the output of the GSR are exit ports to various parts of the data center. The GSR is generally very high bandwidth and generally does not crash. The gateway 26 is placed behind the GSR and across some or all of the output ports of the GSR into the data center. This configuration allows the gateway 26 to monitor and control some or all of the traffic entering the data center without the need to provide routing functionality.

Alternatively, a gateway 26 can tap a network line without being deployed physically in line, and it can control network traffic, for example, by dynamically installing filters on nearby routers. The gateway 26 would install these filters on the appropriate routers via an out of band connection, i.e. a serial line or a dedicated network connection. Other arrangements are of course possible.

Referring to FIG. 3, data collectors 28 are shown coupled to the network to tap or sample traffic from data centers 20 a-20 c. Although data collectors 28 can be dispersed throughout the network 14 they can be strategically disposed at peering points, i.e., points where network traffic from two or more different backbone providers meet. The data collectors 28 can also be disposed at points of presence (PoPs). The data collectors 28 monitor and collect information pertaining to network traffic flow. The data collectors process statistics based on monitored network traffic that enters a peering point. Data collectors 28 include a monitoring process 32 (FIG. 6) as well as a communication process that communicates data to the control center over the hardened network 30. One or more data collector devices 28 use the monitoring process to monitor one or more lines that enter the peering point. Each data collector 28 would be able to monitor one or more lines depending on the specifics of how the network is configured and bandwidth requirements.

The gateway 26 and data collector 26 are typically software programs that are executed on devices such as computers, routers, or switches. In one arrangement, packets pass through the gateway 26 disposed at the data center 22 a and are sampled by the data collector.

Referring to FIG. 4, the data collector 26 performs 40 a sampling and statistic collection process 40. The data collector samples 42 one (1) packet in every (n) packets and has counters to collect statistics about every packet. The data collector 26 parses the information in the sampled packet. Information collected includes source information 44, which may be fake or spoofed, e.g., not correct information. It will also include destination information 46, which generally is accurate information. The data collector 28 collects that information but need not log the sampled packets. The data collector 28 maintains a log over a period of time, e.g., in the last hour. As an example, the log that the data collector 26 maintains is a log that specifies that the data collector has seen a certain number of packets, e.g., 10,000 packets of a particular kind, that apparently originated from a particular source(s) that are going to a particular destination.

Based on rules 48 within the data collector 26, the data collector 26 analyzes 50 the collected statistics and may if necessary compose 52 a message that raises an alarm. Alternatively, the data collector can respond to queries concerning characteristics of traffic on the network. Typically, the queries can be for information pertaining to statistics. It can be in the form of an answer to a question e.g., how many packets of a type did the data collector see or it can be a request to down load via the hardened network, the entire contents of the log. One rule is that when the data collector 26 starts sampling, the data collector periodically logs data and produces a log of a large plurality of different network flows over a period of time.

Referring to FIG. 5, a deployment for the control center 24 is shown. The control center 24 receives information from one or more gateways 26 and data collectors 28 and performs appropriate analysis using an analysis process 62. The control center is a hardened site.

The control center 24 has multiple upstream connections so that even during an attack it will have other ways to couple to the network 30. Several approaches can be used to harden the site. One approach can use special software between the site and the Internet 14 to make it immune to attack. An approach is to have a physically separate network 30 connected to all of the devices, e.g., gateways 26 and data collectors 28. One exemplary embodiment of that physically separate network 30, which is hardened, is the telephone system. Thus, each one of the data collectors 26 and gateways 26 includes an interface to the separate network, e.g., a modem. The data center 26 also includes a corresponding interface to the separate network, e.g., a modem or a modem bank 60.

With this approach, the redundant network 30 is not accessible to the attacker. The redundant network 30 thus is available to communicate between the data center 24 and data collectors and gateways to coordinate response to an attack. In essence, the network 30 used by the data center to communicate with the data collectors 26 and gateways 26 is not available to the attacker. Alternatively, if less than complete assurance is required, the control center could be resistant to attack and still be connected to the Internet 14.

The analysis process 62 that is executed on the control center 24 analyzes data from the gateways 26 and data collectors 28. The analysis process 62 tries to detect attacks on victim sites. The analysis process 62 views attacks as belonging to, e.g., one of three classes of attack. Herein these classes of attack are denoted as low-grade with spoofing, low-grade without spoofing and high-grade whether spoofing or non-spoofing.

A low-grade attack is an attack that does not take out upstream bandwidth. A low-grade attack does not significantly overburden the links between the Internet 14 and the victim data center 12. The low-grade non-spoofing attack is the simplest type of attack to defend against. It simply requires identifying the source of the attack and a mechanism to notify an administrator at the victim site to install a filter or filters at appropriate points to discard traffic containing the source address associated with the attack.

With a low-grade spoofing-type attack, an attacker sends an IP-packet to a destination but fakes the source address. There is no way to enforce use of an accurate source address by a sender. During a spoofing attack, each one of the attacking machines will send a packet with a fake, e.g., randomly selected or generated source address. Under this type of attack, the victim 12 alone cannot thwart the attack. An administrator at the victim 12 can try to put a filter on a router to stop the packets. However, there is no way for the administrator to guess what the random address of the next packet will be.

The control center 24 also includes a communication process 63 to send data to/from the gateways 26 and data collectors 28. The gateway 26 at the victim 12 contacts the control center and notifies the control center 24 that the victim 12 data center is under a spoofing attack. The gateway 26 identifies itself by network address (e.g., static IP address if on the Internet 14), via a message to the control center 24. The message sent over the hardened network 30 indicates the type of attack, e.g., an attack from addresses that the victim 12 cannot stop because it is a spoofing type of attack. The control center queries data collectors 28 and asks which data collectors 28 are seeing suspicious traffic being sent to the victim 12.

The packets from the attacker will have faked source addresses that will be changing with time. However, the control center can issue a query for this kind of packet by victim destination address. The data collectors 28 reply with the information collected. Based on that collected information from the data collectors 28, the control center can then determine what data centers are performing the spoofing on the victim 12.

In the present configuration, there are two possible sources of attack traffic: either the attacker is behind a gateway 26 or not. If the attacker is behind a gateway 26, the control center issues a request to the appropriate gateway 26 to block the attacking traffic, e.g. by allowing the appropriate gateway 26 to discard traffic, e.g., packets that contain the victim 12 destination address. The gateway 26 stops that traffic in a transparent manner. If the attacker is not behind a gateway 26, data collectors 28 are used to provide information about possible locations of the attackers. The availability of information from data collectors 28 increases the speed with which attackers are discovered. The data collectors 28 are positioned at network switching points that see a high volume of traffic, which minimizes the required number of deployed data collectors.

The high-grade attacks are attacks that take out the link between the victim 12 data center and the Internet 14. With a high-grade attack it does not matter whether the victim 12 is spoofed or not. Under a high-grade attack, the attack requires cooperation just like the low grade spoofing attack. Thus, the same thwarting mechanism is used for either spoofing or non-spoofing, e.g., using information from the data collectors 28 to identify attacking networks. This information is used to either automatically shutdown traffic having the victim's destination address at the appropriate gateways 26 or is used to identify networks or data centers from which the attack is originating and to follow up with calls to the appropriate administrators.

Referring to FIG. 6, a monitoring process 32 is shown. The monitoring process 32 can be deployed on data collectors 28 as well as gateways 26. The monitoring process 32 includes a process 32 a to collect statistics of packets that pass by the data collectors 28 or through the gateways 26. The monitoring process 32 also includes several processes 32 b to identify, malicious traffic flows based on the collected statistics as further described below.

Referring to FIG. 7, the gateways 26 and data collectors 28 are capable of looking at multiple levels of granularity. The gateways 26 and data collectors have monitoring process 32 used to measure some parameter of traffic flow. One goal of the gateways 26 and data collectors 28 is to measure some parameter of network traffic. This information collected by the gateways 26 and data collectors is used to trace the source of an attack.

One of the algorithms to measure parameters of traffic flow divides the traffic flow into buckets. For example, consider one simple parameter, the count of how many packets a data collector or gateway examines. An algorithm to track the count of this parameter starts with a predefined number of buckets, e.g., “N” buckets. The buckets are implemented as storage areas in the memory space of the data collector or gateway device. The algorithm will use some hash function “f(h)”, which takes the packet and outputs an integer that corresponds to one of the buckets “B₁-B_(N)”. Statistics from the packets start accumulating in the buckets “B₁-B_(N)”. The buckets “B₁-B_(N)” are configured with threshold values “Th.” As the contents of the buckets B₁-B_(N) reach the configured thresholds values “Th”, (e.g., compare values of packet count or packet rate to threshold), the monitoring process 32 deems that event to be of significance. The monitoring process 32 takes that bucket, e.g., B_(i) and divides that bucket B_(i) into some other number M of new buckets B_(i1)-B_(iM). Each of the new buckets B_(i1)-B_(iM) contains values appropriately derived from the original bucket B_(i). Also, the hash function is extended to map to N+M−1 “h→N+M−1” values, rather than the original N values.

An attack designed to use the algorithm of FIG. 6 against a gateway 26 or a data collector 28 might send packets in such a fashion as to explode the number of buckets. Since each bucket consumes memory space, the attack can be designed to consume all available memory and crash the device, e.g., computer on which the monitoring process 32 executes. There are ways of preventing that type of attack on the monitoring process 32. One way is to make the hash function change periodically, e.g., randomly. Also the hash function is secret so that the packets are reassigned to different buckets in ways unknown to the attackers.

Referring to FIG. 8, a second method is that instead of using just thresholds and values inside a given bucket, the monitoring process 32 also sets thresholds on the number of buckets. As the gateway 26 or data collector 28 approaches a bucket threshold “Th”, the gateway 26 or data collector 28 have the ability to take several buckets B₁-B₃ and divide them in more buckets B₁-B₄ or combine them into fewer bucket B₁-B₂.

The function of the variable number of buckets is to dynamically adjust the monitoring process to the amount of traffic and number of flows, so that the monitoring device (e.g., gateway 26 or data collector 28) is not vulnerable to DoS attacks against its own resources. The variable number of buckets also efficiently identifies the source(s) of attack by breaking down traffic into different categories (buckets) and looking at the appropriate parameters and thresholds in each bucket.

Thus, with multi-level analysis as discussed in FIGS. 6 and 7, traffic is monitored at multiple levels of granularity, from aggregate to individual flows. Multi-level analysis can be applied to all types of monitoring (i.e. TCP packet ratios, repressor traffic, etc. discussed below) except TCP SYN proxying (because the latter requires per-connection monitoring of all half-open connections as discussed below).

The monitoring process 32 has the gateway 26 or the data collectors 28 keep track of a metric (such as packet ratio) for each of n traffic buckets. (If n=1, the monitoring process 32 tracks the metric for all traffic in the aggregate.) The monitoring process 32 places packets into buckets according to a hash function of the source or destination address. If the metric in any bucket exceeds a given “suspicious” threshold, that bucket is split into several smaller buckets, and the metric is tracked individually for each new bucket. In the limit, each bucket can correspond to a single flow (source address/port and destination address/port pair). The resulting per-flow monitoring is resilient to denial-of-service attacks. If the number of buckets exceeds a given memory limit (for example, due to a many-flow spoofing attack), several fine-grain buckets can be aggregated into a single coarse-grain bucket. The hash function for placing packets into traffic buckets is secret and changes periodically, thwarting attacks based on carefully chosen addresses.

In the worst case, an attacker actually spoofs packets from all possible addresses. An IP address, for example is 32 bits long. This address length allows for approximately 4 billion possible random addresses and makes it impossible for the gateway at the victim site 12 to identify the attacker. In that worst case, the gateway 26 calls the control center, indicates the address of the gateway 26, and conveys that the gateway 26 is receiving unreasonably high levels of random traffic. The control center 24 contacts the data collectors 28. The control center 24 analyzes the statistics collected by the data collectors 28 to try to determine the source of the traffic.

Egress filtering is a recommended Internet 14 best practice procedure that does not allow any packets out of a network unless the source address belongs to that network. Egress filtering prevents hosts on that network from sending out packets with completely random source addresses. Rather, the space of usable fake addresses is limited by the size of the host's network address space, and may range up to 24 bits rather than the full 32 bits. If an attacker is attacking from a network that performs egress filtering, then all the attack traffic reaching a victim will fall into a smaller number of buckets, those corresponding to the source network address. In this way, the gateway 26 can identify the approximate source of the attack without necessarily relying on the control center or data collectors.

Several methods can be used separately or in combination to identify, malicious traffic flows. For example, the gateway 26 can detect DoS attacks and identify malicious flows or source addresses using at least one or more of the following methods including: analyzing packet ratios of TCP-like traffic; analyzing “repressor” traffic for particular types of normal traffic; performing TCP handshake analysis; performing various types of packet analysis at packet layers 3-7; and logging/historical analysis.

Packet Ratios for TCP-Like Traffic.

The Transmission Control Protocol (TCP) is a protocol in which a connection between two hosts, a client C, e.g. a web browser, and a server S, e.g. a web server, involves packets traveling in both directions, between C and S and between S and C. When C sends data to S and S receives it, S replies with an ACK (“acknowledgement”) packet. If C does not receive the ACK, it will eventually try to retransmit the data to S, to implement TCP's reliable delivery property. In general, a server S will acknowledge (send an ACK) for every packet or every second packet.

Referring to FIG. 9, the monitoring process in the gateway 26 can examine 82 a ratio of incoming to outgoing TCP packets for a particular set of machines, e.g. web servers. The monitoring process can compare 84 the ratio to a threshold value. The monitoring process can store 86 this ratio, time stamp it, etc. and conduct an ongoing analysis 88 to determine over time for example how much and how often it exceeds that ratio. As the ratio grows increasingly beyond 2:1, it is an increasing indication that the machines are receiving bad TCP traffic, e.g. packets that are not part of any established TCP connection, or that they are too overloaded to acknowledge the requests. This ratio is one of the parameters measured using the multiple-bucket algorithm described previously.

The gateway 26 divides traffic into multiple buckets, e.g. by source network address, and tracks the ratio of ingoing to outgoing traffic for each bucket. As the ratio for one bucket becomes skewed, the gateway 26 may subdivide that bucket to obtain a more detailed view. The gateway 26 raises 90 a warning or alarm to the data center 24 and/or to the administrators at the victim site 12.

Repressor Traffic

The phrase “repressor traffic” as used herein refers to any network traffic that is indicative of problems or a potential attack in a main flow of traffic. A gateway 26 may use repressor traffic analysis to identify such problems and stop or repress a corresponding attack.

One example of repressor traffic is ICMP port unreachable messages. These messages are generated by an end host when it receives a packet on a port that is not responding to requests. The message contains header information from the packet in question. The gateway 26 can analyze the port unreachable messages and use them to generate logs for forensic purposes or to selectively block future messages similar to the ones that caused the ICMP messages.

TCP Handshake Analysis

A TCP connection between two hosts on the network is initiated via a three-way handshake. The client, e.g. C, sends the server, e.g. S, a SYN (“synchronize”) packet. S the server replies with a SYN ACK (“synchronize acknowledgment”) packet. The client C replies to the SYN ACK with an ACK (“acknowledgment”) packet. At this point, appropriate states to manage the connection are established on both sides.

During a TCP SYN flood attack, a server is sent many SYN packets but the attacking site never responds to the corresponding SYN ACKs with ACK packets. The resulting “half-open” connections take up state on the server and can prevent the server from opening up legitimate connections until the half-open connection expires, which usually takes 2-3 minutes. By constantly sending more SYN packets, an attacker can effectively prevent a server from serving any legitimate connection requests.

Referring to FIG. 10, in an active configuration, a gateway 26 can defend against SYN flood attacks. During connection setup, the gateway forwards 102 a SYN packet from a client to a server. The gateway forwards 104 a resulting SYN ACK packet from a server to client and immediately sends 106 ACK packet to the server, closing a three-way handshake. The gateway maintains the resulting connection for a timeout period 108. If the ACK packet does not arrive from client to server 110, the gateway sends 112 a RST (“reset”) to the server to close the connection. If the ACK arrives 114, gateway forwards 116 the ACK and forgets 118 about the connection, forwarding subsequent packets for that connection. A variable timeout 120 period can be used. The variable time out period can be inversely proportional to number of connections for which a first ACK packet from client has not been received. If gateway 26 is placed inline in the network, when number of non-ACK'ed connections reaches a configurable threshold 122, the gateway will not forward any new SYNs until it finishes sending RSTs for those connections.

In a passive configuration, a gateway 26 can similarly keep track of ratios of SYNs to SYN ACKs and SYN ACKs to ACKs, and raise appropriate alarms when a SYN flood attack situation occurs.

Layer 3-7 Analysis

With layer 3-7 analysis, the gateway 26 looks at various traffic properties at network packet layers 3 through 7 to identify attacks and malicious flows. These layers are often referred to as layers of the Open System Interconnection (OSI) reference model and are network, transport, session, presentation and application layers respectively. Some examples of characteristics that the gateway may look for include:

1. Unusual amounts of IP fragmentation, or fragmented IP packets with bad or overlapping fragment offsets.

2. IP packets with obviously bad source addresses, or ICMP packets with broadcast destination addresses.

3. TCP or UDP packets to unused ports.

4. TCP segments advertizing unusually small window sizes, which may indicate load on server, or TCP ACK packets not belonging to a known connection.

5. Frequent reloads that are sustained at a rate higher than plausible for a human user over a persistent HTTP connection.

Logging and Historical Traffic Analysis

The gateways 26 and data collectors 28 keep statistical summary information of traffic over different periods of time and at different levels of detail. For example, a gateway 26 may keep mean and standard deviation for a chosen set of parameters across a chosen set of time-periods. The parameters may include source and destination host or network addresses, protocols, types of packets, number of open connections or of packets sent in either direction, etc. Time periods for statistical aggregation may range from minutes to weeks. The device will have configurable thresholds and will raise warnings when one of the measured parameters exceeds the corresponding threshold.

The gateway 26 can also log packets. In addition to logging full packet streams, the gateway 26 has the capability to log only specific packets identified as part of an attack (e.g., fragmented UDP packets or TCP SYN packets that are part of a SYN flood attack). This feature of the gateway 26 enables administrators to quickly identify the important properties of the attack.

Building a DoS-Resistant Network

The network of gateways 26, data collectors 28, and control center 24 are made DoS resistant by combining and applying several techniques. These techniques include the use of SYN cookies and “hashcash” to make devices more resistant to SYN floods and other attacks that occur at connection setup time. Also, the data center can use authentication and encryption for all connections. Private/public key pairs are placed on machines before deployment to avoid man-in-the-middle attacks. The control center 24 can have multiple physical connections from different upstream network service providers. The network over which the data center communicates between gateways and data collectors is a private redundant network that is inaccessible to attackers.

Information exchange between gateways/data collectors and the control center is efficient by transferring only statistical data or minimal header information, and by compressing all data.

This application includes an APPENDIX A attached hereto and incorporated herein by reference. APPENDIX A includes Click code for monitor software.

This application also includes an APPENDIX B attached hereto and incorporated herein by reference. APPENDIX B sets out additional modules for a Click Router that pertains to thwarting DoS attacks. “Click” is a modular software router system developed by The Massachusetts Institute of Technology's Parallel and Distributed Operating Systems group. A Click router is an interconnected collection of modules or elements used to control a router's behavior when implemented on a computer system.

Other embodiments are within the scope of the appended claims. 

1. A machine implemented method of monitoring traffic flow in a monitoring device disposed to receive network traffic packets, the method comprising: producing statistics corresponding to a parameter of traffic flow to trace the source of an attack, with producing further comprising: mapping the traffic flow into a plurality of buckets by applying a hash function “f(h)” to the parameter of the traffic flow to output an integer corresponding to one of the buckets; accumulating statistics from the packets; and comparing the number of buckets to a threshold; and adjusting the number of buckets based on determining whether the number of buckets should be divided into more buckets or combined into fewer buckets based on comparing the number of buckets to the threshold.
 2. The method of claim 1 wherein the buckets are storage areas in a memory space of the monitor device.
 3. The method of claim 1 wherein as the number of buckets changes, the buckets have values derived from the buckets prior to the change.
 4. The method of claim 1 wherein the hash function adapts to map to the new number of buckets, as the new number of buckets changes.
 5. The method of claim 1 wherein comparing statistic values comprises: comparing the value accumulated in the bucket to a threshold that depends on the number of buckets.
 6. The method of claim 1 wherein the parameter is the count of how many packets a data collector or gateway examines.
 7. The method of claim 1 wherein as a value of a parameter for one bucket approaches a threshold, the monitoring device raises an alarm.
 8. The method of claim 1 wherein the hash function changes periodically in a randomly secret manner so that packets are reassigned to different buckets.
 9. The method of claim 1 wherein the variable number of buckets dynamically adjusts the amount of traffic and number of flows monitored, so that the monitoring device is not vulnerable to a denial of service attack against its own resources.
 10. The method of claim 1 wherein the variable number of buckets efficiently identifies the source or sources of attack by breaking down traffic into different buckets and examining statistics accumulated for a parameter and a corresponding threshold in each bucket.
 11. The method of claim 1 wherein the traffic is monitored at multiple levels of granularity, from aggregate to individual flows.
 12. The method of claim 1 wherein the method is applied to monitoring of TCP packet ratios and repressor traffic.
 13. The method of claim 1 wherein the threshold is a first threshold and the method further comprises: comparing accumulated statistic values from the buckets to second threshold values to determine that an event is of significance.
 14. A computer program product residing on a computer readable storage medium for monitoring network traffic flow in a network, the computer program product comprising instructions for causing a computer to: map, on a computer, traffic flow into a plurality of buckets by applying a hash function “f(h)” to a parameter of the traffic flow to output an integer corresponding to one of the buckets; accumulate statistics from the packets; and compare the accumulated statistic values from the buckets to configured threshold values corresponding to the number of buckets to determine that an event is of significance; and adjust the number of buckets as the number of buckets approaches a second threshold.
 15. The computer program product of claim 14 wherein based on the second threshold, the buckets are divided into more buckets or combined into fewer buckets.
 16. The computer program product of claim 14 wherein instructions to monitor further comprise instructions to divide the bucket into a different number of new buckets containing values derived from the original bucket.
 17. The computer program product of claim 14 wherein the hash function adapts to map to the new number of buckets as the new number of buckets changes.
 18. The computer program product of claim 14 wherein the parameter is the count of how many packets a data collector or gateway examines.
 19. The computer program product of claim 14 wherein the buckets are storage areas in the memory space of the monitor device.
 20. The computer program product of claim 14 wherein the hash function changes periodically a randomly secret manner so that packets are reassigned to different buckets.
 21. A data collector to collect statistical information about network flows comprising: a computer read ante medium; a computing device with a processor that executes a computer program product stored on the computer readable medium comprising instructions to cause the computing device to: map traffic flow into a plurality of buckets by applying a hash function “f(h)” to the parameter of the traffic flow to output an integer corresponding to one of the buckets; accumulate statistics from the packets; and compare the accumulated statistic values from the buckets to configured threshold values corresponding to the number of buckets to determine that an event is of significance; and adjust the number of buckets as the number of buckets approaches a second threshold.
 22. The data collector of claim 21 wherein based on the second threshold, the buckets are divided into more buckets or combined into fewer buckets.
 23. The data collector of claim 21 wherein instructions to monitor further comprises instructions to divide the bucket into a different number of new buckets containing values, derived from the original bucket.
 24. The data collector of claim 21 wherein the hash function adapts to map to the new number of buckets as the new number of buckets changes.
 25. The data collector of claim 21 wherein the parameter is the count of how many packets the data collector examines.
 26. The data collector of claim 21 wherein the buckets are storage areas in the memory space of the monitor device.
 27. The data collector of claim 21 wherein the hash function changes periodically in a randomly secret manner so that packets are reassigned to different buckets.
 28. The data collector of claim 21 wherein instructions to compare statistic values comprises instructions to: compare the value accumulated in the bucket to a threshold that depends on the number of buckets.
 29. The data collector of claim 21 wherein as a value of a parameter for one bucket approaches a threshold, the monitoring device raises an alarm.
 30. The data collector of claim 21 wherein the variable number of buckets dynamically adjusts the amount of traffic and number of flows monitored, so that the data collector is not vulnerable to a denial of service attack against its own resources.
 31. The data collector of claim 21 wherein the variable number or buckets efficiently identities the source or sources of attack by breaking down traffic into different buckets and examining statistics accumulated for a parameter and a corresponding threshold in each bucket.
 32. The data collector of claim 21 wherein the traffic is monitored at multiple levels of granularity, from aggregate to individual flows.
 33. The data collector of claim 21 wherein the traffic is applied to monitoring of TCP packet ratios and repressor traffic.
 34. The data collector of claim 21 wherein the threshold is a first threshold and the computer program further comprises instructions to: compare accumulated statistic values from the buckets to second threshold values to determine that an event is of significance.
 35. The data collector of claim 21 further comprising: a port to link the data collector to a central control center.
 36. A method of monitoring traffic flow in a monitor device disposed to receive network packets, the method comprising: producing statistics corresponding to a parameter of the traffic flow to trace a source of an attack, with producing further comprising: mapping the traffic flow into a plurality of buckets; varying the number of buckets according to the amount of traffic and number of flows to breakdown traffic, flow into different buckets; and analyzing statistics accumulated for a parameter and a corresponding threshold in the bucket to identify the source of the attack.
 37. The method of claim 35 wherein varying varies the number of buckets so that the monitor device is not vulnerable to DoS attacks against its own resources.
 38. The method of claim 35 wherein varying the number of buckets comprises: comparing the number of buckets to a threshold number or buckets; determining whether the number of buckets should be divided into more buckets or combined into fewer buckets based on comparing the number of buckets to the threshold and as the number of buckets changes, the buckets have values derived from the buckets prior to the change.
 39. The method of claim 36 wherein further comprising: comparing accumulated statistic values from the buckets to second threshold values to determine that an event is of significance.
 40. The method of claim 36 wherein comparing statistic values comprises: accumulating statistic values from the packets; and comparing the values accumulated in the buckets to thresholds that depend on the number of buckets.
 41. The method of claim 36 wherein the variable number of buckets dynamically adjusts the amount of traffic and number or flows monitored, so that the monitoring device is not vulnerable to a denial of service attack against its own resources.
 42. The method of claim 36 wherein the buckets are storage areas in a memory space of the monitor device and mapping the traffic flow into a plurality of buckets comprises: applying a hash function “f(h)” to the parameter of the traffic flow to output an integer corresponding to one of the buckets.
 43. A computer program product reaming on a computer readable medium for monitoring traffic flow in a monitor device disposed to receive network packets, the commuter program product comprises instructions for causing the device to: produce, on a computer, statistics corresponding to a parameter of the traffic flow to trace a source of an attack, with producing further comprising: map the traffic flow into a plurality of buckets; vary the number of buckets according to the amount of traffic and number of flows to breakdown the traffic flow into different buckets; and analyze statistics accumulated for a parameter and a corresponding threshold in the bucket to identify a source of the attack.
 44. The computer program product of claim 43 wherein instructions to vary, vary the number of buckets so that the monitoring device is not vulnerable to DoS attacks against its own resources.
 45. The computer program product of claim 43 wherein instructions to vary comprises instructions to: compare the number of buckets to a threshold number of buckets; determine whether the number of buckets should be divided into more buckets or combined into fewer buckets based on comparing the number of buckets to the threshold and as the number of buckets changes, the buckets have values derived from the buckets prior to the change.
 46. The computer program product of claim 43 further comprising instructions to: compare accumulated statistic values from the buckets to second threshold values to determine that an event is of significance.
 47. The computer program product of claim 43 wherein instructions to compare statistic values comprises instructions to: accumulate statistic values from the packets; and compare the values accumulated in the buckets to thresholds that depend on the number of buckets.
 48. The computer program product of claim 43 wherein the variable number of buckets dynamically adjusts the amount of traffic and number of flows monitored, so that the monitoring device is not vulnerable to a denial of service attack against its own resources.
 49. The computer program product of claim 43 wherein the buckets are storage areas in a memory space of the monitor device and instructions to map the traffic flow into a plurality of buckets comprises instructions to: apply a hash function “f(h)” to the parameter of the traffic flow to output an integer corresponding to one of the buckets. 